While Lotfi has indicated, in many of his talks, that fuzzy logic is a
methodology for adding the calculus of imprecision to any discipline, I
still believe that we must look at the informational gain in fuzzy systems
rather than simply some method of generating values in the [0,1] interval.
Fuzzy logic has become a catch-all phrase for many families of interval
arithmetic and for any arithmetic method that generates anything other than
a finite set of integer valued solutions (I recall seeing an investment
consultation system that proclaimed it used "FUZZY LOGIC -- the same kind of
logic found in Mycin and other expert systems!" Of course we know that Mycin
used a form of semi-adhoc Bayesian evidence calculus (certainty factors)
developed by Bruce Buchanan and Ed Shortliffe).

As I said in a previous post, it is not enough to simply develop a "fuzzy"
measure of some system state. We need to know how the approximate reasoning
or implication mechanism uses fuzzy logic to increase knowledge about the
model or system state. In my opinion, the fuzzy string matching machine
tells us something about the error space between two strings but tells us
nothing about the relationship between the two strings in a way that would
be meaningful in a generalized machine reasoning context (although,
obviously, it would be very helpful in a specific application concerned with
error detection, such as spell checkers). This confusion of state
measurement with knowledge is a common problem when we deal with ideas
related to fuzzy logic. Confusing numerical with contextual, semantic, or
labeled ambiguity is another case in point. If we ask, "How many bakers are
in the army?" -- do we mean people with the name Baker or people with the
MOS of Baker (MOS = military occupational specialty, a job type)? Many posts
to this news group ask about handling such non-numeric ambiguity without
considering the infrastructure necessary to resolve these kinds of
ambiguity.

In any case, in the cemetery vs cemetary case, we might ask "to what degree
is X like Y" and in this one instance we receive a pretty good answer. But
is the answer generalizable to a fuzzy string matching function? If we pick
words at random from a dictionary and then evaluate them using the same
function, what is the outcome? Even when we hit close strings, comparing
"hat" and "cat" or "house" and "mouse" or "Street" and "strut" all we know
is that we have two strings that are in general (or remote) proximity to
each other in terms of the underlying lexicography. For a spell checker this
is a good thing to know. But if you are writing a general intelligent
business system, with few specific and narrow exceptions, this kind of
"fuzzy" metric is meaningless.

Where is the fuzzy logic in this?

Naturally, this is my own opinion. The fact that I am infallible in my
pronouncements about fuzzy logic, should not deter anyone from expressing
their own conflicting opinions, however wrong they might be! ;-)

Earl

Will Dwinnell <76743.1740@CompuServe.COM> wrote in message ...>I (Will Dwinnell) wrote:>"I submit that from another perspective, this algorithm does>involve fuzzy logic: the presented algorithm gives a fuzzy>membership in the class of strings that are "like" the target>string.">>Jared Boehm responded:>"I'll settle for fuzzy-like :) In hindsight, there are some ways>in which it follows some principles of fuzzy logic, but in quite>a few ways it differs from what is considered fuzzy logic.">>I'm not surewhy you think it differs. Keep in mind that Zadeh>described a fuzzy calculus which included many different>analytical tools. The simplest fuzzy logical systems (like your>string-matching algorithm) merely attach single fuzzy truth>values to items such as rule antecedents or consequents. All the>business with fuzzy distributions and so on are also fuzzy logic,>but they are not necessary to be fuzzy logic.>>-->Will Dwinnell

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